CSCI 4560/6560 Evolutionary Computation and Its Applications

Fall 2008: Tuesdays and Thursdays 3:30pm - 4:45pm, Wednesdays 3:35pm - 4:25pm, Boyd GSRC 306

Instructor: Prof. Khaled Rasheed
Telephone: 542-3444
Office Hours: Tuesday: 1-2:30pm and Wednesday: 4:35-6:00pm or by email appointment
Office Location: Room 219B, Boyd GSRC
Email: khaled@cs.uga.edu

Teaching Assistant: Chandana Kaza
Office Hours: Tuesday: 2:00-3:00pm and Thursday: 2:00-3:00pm or by email appointment
Office Location: Room 538, Boyd GSRC
Email: kaza@uga.edu

Objectives:

To provide a broad introduction to the field of Genetic Algorithms and other fields of Evolutionary Computation and global optimization. To teach students how to apply these methods to solve problems in complex domains. The course is appropriate both for students preparing for research in Evolutionary Computation, as well as Science and Engineering students who want to apply Evolutionary Computation techniques to solve problems in their fields of study.

Recommended Background:

CSCI 2720 Data Structures (or permission of the instructor). Familiarity with basic computer algorithms and data structures and at least one high level programming language.

Topics to be Covered:

Genetic Algorithm core topics including representation, operators and architectures. Other fields of evolutionary computation including Genetic Programming, Evolution Strategies, Evolutionary Programming and Classifier Systems. Evolutionary Computation applications in science and Engineering. Other nature-inspired global optimization techniques.

Expected Work:

Reading; assignments (including programming); midterm; final; and term project and paper. (Unless otherwise announced by the instructor: all assignments and all exams must be done entirely on your own.)

Academic Honesty and Integrity:

All academic work must meet the standards contained in "A Culture of Honesty." Students are responsible for informing themselves about those standards before performing any academic work. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense.

Grading Policy:

Assignments: 30% (Programs, questions, paper presentations)

Midterm Examination: 20%

Final Examination: 25%

Term Project: 25% (includes term paper and presentation)
Students may work on their term projects individually or in groups of up to THREE students each. The above distribution is only tentative and may change later. The instructor will announce any changes.

Assignment Submission Policy

Assignments must be turned in by the assigned deadline. Late assignments will not be accepted. Rare exceptions may be made by the instructor only under extenuating circumstances and in accordance with the university policies.

Course Home-page

A variety of materials will be made available on the EC Class Home-page at http://www.cs.uga.edu/~khaled/ECcourse/, including handouts, lecture notes and assignments. Announcements may be posted between class meetings. You are responsible for being aware of whatever information is posted there.

Lecture Notes

Copies of some of Dr. Rasheed's lecture notes will be available at the bottom of the class home page. Not all the lectures will have electronic notes though and the students should be prepared to take notes inside the lecture at any time.

Textbook in Bookstore

"Introduction to Evolutionary Computing", Eiben and Smith. Springer-Verlag, New York, 2003. (Required)

Additional Books

"Genetic Algorithms in Search, Optimization, and Machine Learning", David Goldberg. Addison-Wesley, 1989.

"An Introduction to Genetic Algorithms", Melanie Mitchell. MIT Press, 1996.

"Genetic Algorithms + Data Structures = Evolution Programs", Zbigniew Michalewicz. Springer-Verlag, New York,1996.

"Evolutionary Computation", D. Dumitrescu et al. CRC Press, 2000.

"Evolutionary Computation 1", Thomas Back et al. IOP Publishing, 2000.

Web Resources

THE HITCH-HIKER'S GUIDE TO EVOLUTIONARY COMPUTATION

GA software packages from the CMU Artificial Intelligence repository

Web archived version of the GA source codes at GALIST archives

Announcements:

[9-18-2008] For the second homework assignment I have some clarifications:
* For the subset sum problem you need not solve for values of N larger than 10000. Doing so will not change your grade and may lead to overflow problems. Also please include in your report the set and the subset for a small value of N around 10 or so. This will allow the TA to manually check your answer. Also please include your email address on the report so that the TA can ask for your code if needed. Finally, to avoid running infinitely for large values of N, you may generate the target sum by going through the list elements one by one and randomly deciding whether or not to include the element in that sum. You will therefore ensure that there is at least one solution. Of course in your program you will pretend that you did not know which elements constituted the target sum. It will be interesting to see if you end up with the same elements or different ones that have the same sum.
* When you code the fitness function for the TSP make sure you include the last edge of the tour (the one taking you back to the starting city) in your calculation of the tour length. You will get very misleading results if you forget that.
* The reports should be turned in as printouts rather than electronically. If the TA needs any electronic materials she will email you.
* If you have any other questions you can email me. I will try to answer within 24 hours.